Representative Chain-of-Reasoning for Aspect Sentiment Quad Prediction
Abstract: Aspect Sentiment Quad Prediction (ASQP) is a crucial sentiment analysis task that has attracted increasing attention. The most recent studies focus on generating complete sentiment quadruples through end-to-end generative models. However, these methods heavily depend on labeled data quality and quantity, performing poorly in low-resource scenarios and less suitable for real-world applications. To address these issues, we propose a novel Representative Chain-of-Reasoning framework (RCR), with the aim of providing representative knowledge for large language models (LLMs) and fully activating their reasoning capabilities for ASQP. Specifically, we develop a Chain Prompting (ChaPT) module to decompose the ASQP task into three subtasks using the stepby-step reasoning mechanism. Then, a Representative Demonstration Retriever (RepDR) is introduced to provide ChaPT with representative demonstrations, balancing diversity and similarity, and enhancing the reasoning capabilities of LLMs at each step. Experimental results confirm the superiority of RCR in both zero-shot and few-shot scenarios, significantly surpassing existing counterparts.
Paper Type: Long
Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Research Area Keywords: emotion detection and analysis,NLP in resource-constrained settings,few-shot generation
Contribution Types: Approaches to low-resource settings
Languages Studied: English
Submission Number: 2494
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